sis 24(3):

Research Article

Improved Nuclear Reaction Heuristic Intelligence Algorithm for Online Learning in Self-Monitoring Strategy Convergence

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  • @ARTICLE{10.4108/eetsis.4848,
        author={Fengjun Liu and Yang Lu and Bin Xie and Lili Ma},
        title={Improved Nuclear Reaction Heuristic Intelligence Algorithm for Online Learning in Self-Monitoring Strategy Convergence},
        journal={EAI Endorsed Transactions on Scalable Information Systems},
        volume={11},
        number={3},
        publisher={EAI},
        journal_a={SIS},
        year={2024},
        month={2},
        keywords={fusion approach to English online learning, self-monitoring strategy, nuclear reaction heuristic intelligence algorithm, Cat chaotic mapping strategy},
        doi={10.4108/eetsis.4848}
    }
    
  • Fengjun Liu
    Yang Lu
    Bin Xie
    Lili Ma
    Year: 2024
    Improved Nuclear Reaction Heuristic Intelligence Algorithm for Online Learning in Self-Monitoring Strategy Convergence
    SIS
    EAI
    DOI: 10.4108/eetsis.4848
Fengjun Liu1,*, Yang Lu2, Bin Xie2, Lili Ma3
  • 1: Qing Gong College North University of Science and Technology
  • 2: Qing Gong College North University of Science and Technology, Tangshan 063000, Hebei, China
  • 3: Tangshan Preschool Teachers College Yutian Branch, Tangshan 063000, Hebei, China
*Contact email: yytgxx@126.com

Abstract

INTRODCTION: By analyzing the problem of self-monitoring in English online learning and constructing a strategy-integrated evaluation method, we can not only enrich the theoretical research results of self-monitoring in online learning, but also improve the independent learning ability and self-monitoring ability of students in English online learning. OBJECTIVES: To address the problem of poor optimization performance of current fusion optimization methods.METHODS:This paper proposes an online learning self-monitoring strategy fusion method based on improved nuclear reaction heuristic intelligent algorithm. First, the problems and enhancement strategies of online learning self-monitoring are analyzed; then, the online learning self-monitoring strategy fusion model is constructed by improving the nuclear reaction heuristic intelligent algorithm; finally, the proposed method is verified to be effective and feasible through the analysis of simulation experiments. RESLUTS: The results show that the fusion method of learning self-monitoring strategies on the line at the 20th iteration number starts to converge to optimization with less than 0.1s optimization time, and the error of the statistical score value before and after weight optimization is controlled within 0.05. CONCLUSION:Addressing the Optimization of Convergence of Self-Monitoring Strategies for English Online Learning.